Data Literacy: Introduction to R

.title[
# Data Literacy: Introduction to R
]
.subtitle[
## Outlook
]
.author[
### Veronika Batzdorfer
]
.date[
### 2024-11-21
]

---

---

## Our jou`R`ney this week (hopefully)

<img src="data:image/png;base64,#../img/r_pkgs_mindblowing.png" width="85%" style="display: block; margin: auto;" />
.footnote[[Source](https://res.cloudinary.com/syknapptic/image/upload/v1521320144/tidyverse_meme_oceake.png)]

---

## Resources: Introductory books

[R for Data Science](https://r4ds.had.co.nz/) by Hadley Wickham

[R Cookbook: Proven recipes for data analysis, statistics, and graphics](https://rc2e.com/) by JD Long & Paul Teetor

[Hands-On Programming with R](https://rstudio-education.github.io/hopr/) by Garrett Grolemund

[R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/) by Roger D. Peng

[Quantitative Social Science Data with R](https://uk.sagepub.com/en-gb/eur/quantitative-social-science-data-with-r/book257236) by Brian J. Fogarty

[Introduction to R for Social Scientists - A Tidy Programming Approach](https://www.routledge.com/Introduction-to-R-for-Social-Scientists-A-Tidy-Programming-Approach/Kennedy-Waggoner/p/book/9780367460723) by Ryan Kennedy & Philip D. Waggoner

[Quantitative Social Science: An Introduction in tidyverse](https://press.princeton.edu/books/hardcover/9780691222271/quantitative-social-science) by Kosuke Imai & Nora Webb Williams

---

## Resources: Online courses & tutorials

- Overview of resources
  - [*learnR4free*](https://www.learnr4free.com/) by Mine Dogucu
  - the [*Big Book of R*](https://www.bigbookofr.com/) by Oscar Baruffa

- [*swirl* - Learn `R` in `R`](https://swirlstats.com/)

- Learning `R` (and statistics) with a cute story: [Teacups, Giraffes, & Statistics by Hasse Wallum & Desirée de Leon](https://tinystats.github.io/teacups-giraffes-and-statistics/)

---

## Working with text data in `R`

As with almost everything else, there are many great resources for working with text data in `R`. Two good options (and starting points) are:
- the [`tidytext` package](https://juliasilge.github.io/tidytext/) and the "accompanying" book [*Text Mining with R: A Tidy Approach*](https://www.tidytextmining.com/) by Julia Silge & David Robinson
- the [`quanteda` package](https://quanteda.io/) and its accompanying [tutorials](https://tutorials.quanteda.io/)

- a nice and free self-paced online course is [*Text mining in R for the social sciences and digital humanities*](https://tm4ss.github.io/docs/index.html) by Andreas Niekler and Gregor Wiedemann

---

## What Are Geospatial Data?

.pull-left[
Data with a direct spatial reference `\(\rightarrow\)` **geo-coordinates**
- Information about geometries
- Optional: Content in relation to the geometries

Can be projected jointly in one single space
- Allows data linking and extraction of substantial information
]

.pull-right[
<img src="data:image/png;base64,#../img/fig_geometries.png" width="85%" style="display: block; margin: auto;" />

.tinyisher[Sources: OpenStreetMap / GEOFABRIK (2018), City of Cologne (2014), and the Statistical Offices of the Federation and the Länder (2016) / Jünger, 2019]
]

---

## [Mapping](https://cran.r-project.org/web/packages/mapsf/vignettes/mapsf.html) is so easy nowadays

```r
library(mapsf)

mtq <- mf_get_mtq()

mf_map(x = mtq)
mf_map(x = mtq, var = "POP", type = "prop")
mf_layout(
  title = "Population in Martinique", 
  credits = "T. Giraud; Sources: INSEE & IGN, 2018"
)
```
]

.pull-right[
<img src="data:image/png;base64,#5_2_Outlook_files/figure-html/mapsf-print-1.png" style="display: block; margin: auto;" />
]

Example from: https://riatelab.github.io/mapsf/

---

## 'Web development' using `R`

These days, a lot of `R` packages provide tools originally developed for the web. For example:
- [bookdown](https://pkgs.rstudio.com/bookdown/) enables you to publish books written in `R Markdown` online
- [pkgdown](https://pkgdown.r-lib.org/) does the same for your own `R` packages
- [blogdown](https://pkgs.rstudio.com/blogdown/l) is more general and helps you with creating websites (examples to follow)

---

## Shiny apps

> Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.

https://shiny.rstudio.com/

---

## Example 1: Movie Explorer

---

## Example 2: CRAN explorer

---

## Writing your own `R` packages

.pull-left[
At a certain point (not now!), you may want to consider writing your own `R` package
- useful for creating reproducible code
- great for distributing your work to others

]

.pull-right[
<img src="data:image/png;base64,#../img/r_packages.jpg" width="1381" style="display: block; margin: auto;" />
[Read the book here!](https://r-pkgs.org/)
]

---

## It's straightforward in `RStudio`

---

## Acknowledgements ❤️

All slides were created with the `R` package [`xaringan`](https://github.com/yihui/xaringan) which builds on [`remark.js`](https://remarkjs.com), [`knitr`](http://yihui.name/knitr), and [`R Markdown`](https://rmarkdown.rstudio.com). The exercises were created with the [`unilur` package](https://github.com/koncina/unilur).  
[`woRkshoptools`](https://stefanjuenger.github.io/woRkshoptools/)